Narratives of Systemic Barriers and Accessibility: Poverty, Equity, Diversity, Inclusion, and the Call for a Post-Pandemic New Normal
Bibliographic record
Abstract
This paper captures the intimate, intensely lived, and storied experiences during the pandemic, on teachers’ narratives of teaching and education. The narratives illuminate deep knowledge and insight into pre-existing school systemic barriers prior to the pandemic, and how those same barriers are magnified during the pandemic in what has become a global watershed moment that calls for equity reform in school systems. A narrative theoretical framework is used, as well as an ethic of care framework that informs the study. Issues of poverty, diversity, equity, and inclusion are illuminated, with further focus on topics of technology access, streaming, resilience, and teacher-student identity and relationship. Recommendations to eradicate systemic barriers in schools are explored, highlighting suggestions for equity reform in areas that include: enhancing professional practice; building a school culture of care, and; developing partnerships and relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".